### SAS Data Analysis Examples Zero-inflated Negative Binomial Regression

Zero-inflated negative binomial regression is for modeling count variables with excessive zeros and it is usually for over-dispersed count outcome variables. Furthermore, theory suggests that the excess zeros are generated by a separate process from the count values and that the excess zeros can be modeled independently.

Please note: The purpose of this page is to show how to use various data analysis commands.  It does not cover all aspects of the research process which researchers are expected to do.  In particular, it does not cover data cleaning and checking, verification of assumptions, model diagnostics or potential follow-up analyses.

#### Examples of Zero-inflated Negative Binomial Regression

Example 1. School administrators study the attendance behavior of high school juniors at two schools. Predictors of the number of days of absence include gender of the student and standardized test scores in math and language arts.

Example 2. The state wildlife biologists want to model how many fish are being caught by fishermen at a state park. Visitors are asked how long they stayed, how many people were in the group, were there children in the group and how many fish were caught. Some visitors do not fish, but there is no data on whether a person fished or not. Some visitors who did fish did not catch any fish so there are excess zeros in the data because of the people that did not fish.

#### Description of the Data

Let's pursue Example 2 from above using the dataset fish.sas7bdat

We have data on 250 groups that went to a park.  Each group was questioned about how many fish they caught (count), how many children were in the group (child), how many people were in the group (persons), and whether or not they brought a camper to the park (camper).

In addition to predicting the number of fish caught, there is interest in predicting the existence of excess zeros, i.e., the probability that a group caught zero fish. We will use the variables child, persons, and camper in our model. Let's look at the data.

proc means data = fish mean std min max var;
var count child persons;
run;

The MEANS Procedure

Variable            Mean         Std Dev         Minimum         Maximum        Variance
----------------------------------------------------------------------------------------
count          3.2960000      11.6350281               0     149.0000000     135.3738795
child          0.6840000       0.8503153               0       3.0000000       0.7230361
persons        2.5280000       1.1127303       1.0000000       4.0000000       1.2381687
----------------------------------------------------------------------------------------
ods graphics  / width=4in height=3in border=off;
proc sgplot data = fish;
histogram count /binwidth=1;
run;
ods graphics off;


proc freq data = fish;
tables child persons camper;
run;

                                  Cumulative
child    Frequency     Percent     Frequency      Percent
----------------------------------------------------------
0         132       52.80           132        52.80
1          75       30.00           207        82.80
2          33       13.20           240        96.00
3          10        4.00           250       100.00

Cumulative    Cumulative
persons    Frequency     Percent     Frequency      Percent
------------------------------------------------------------
1          57       22.80            57        22.80
2          70       28.00           127        50.80
3          57       22.80           184        73.60
4          66       26.40           250       100.00

Cumulative    Cumulative
camper    Frequency     Percent     Frequency      Percent
-----------------------------------------------------------
0         103       41.20           103        41.20
1         147       58.80           250       100.00
proc means data = fish mean var n nway;
class camper;
var count;
run;
                  N
camper    Obs            Mean        Variance      N
----------------------------------------------------------
0    103       1.5242718      21.0557777    103
           1    147       4.5374150     212.4009878    147
----------------------------------------------------------

We can see from the table of descriptive statistics above that the variance of the outcome variable is quite large relative to the means. This might be an indication of over-dispersion.

#### Analysis methods you might consider

Before we show how you can analyze this with a zero-inflated negative binomial analysis, let's consider some other methods that you might use.

• OLS Regression - You could try to analyze these data using OLS regression. However, count data are highly non-normal and are not well estimated by OLS regression.
• Zero-inflated Poisson Regression - Zero-inflated Poisson regression does better when the data is not overdispersed, i.e., when variance is not much larger than the mean.
• Ordinary Count Models - Poisson or negative binomial models might be more appropriate if there are not excess zeros.

#### SAS zero-inflated negative binomial analysis using proc genmod

A zero-inflated model assumes that zero outcome is due to two different processes. For instance, in the example of fishing presented here,  the two processes are that a subject has gone fishing vs. not gone fishing. If not gone fishing, the only outcome possible is zero. If gone fishing, it is then  a  count process. The two parts of the a zero-inflated model are a binary model, usually a logit model to model which of the two processes the zero outcome is associated with and a count model, in this case, a negative binomial model, to model the count process. The expected count is expressed as a combination of the two processes. Taking the example of fishing again, E(#of fish caught=k) = prob(not gone fishing )*0 + prob(gone fishing)*E(y=k|gone  fishing).

Now let's build up our model. We are going to use the variables child and camper to model the count in the part of negative binomial model  and the variable persons in the logit part of the model. The SAS commands are shown below. We treat variable camper as a categorical variable by including it in the class statement. This will also make the post estimations easier. In this particular example, we also explicitly want to use camper = 0 as the reference group. To this end, we sort the data in descending order and use the order= option in proc genmod to force it to take camper = 0 as the reference group.

proc sort data = fish;
by descending camper;
run;
proc genmod data = fish order=data;
class camper;
model count = child camper /dist=zinb;
zeromodel persons;
run;
The GENMOD Procedure

Model Information

Data Set                                    WORK.FISH
Distribution          Zero Inflated Negative Binomial
Dependent Variable                              count

Number of Observations Used         250

Class Level Information

Class       Levels    Values

camper           2    1 0

Criteria For Assessing Goodness Of Fit

Criterion                     DF           Value        Value/DF

Deviance                                865.7818
Scaled Deviance                         865.7818
Pearson Chi-Square           245        534.2944          2.1808
Scaled Pearson X2            245        534.2944          2.1808
Log Likelihood                         -432.8909
Full Log Likelihood                    -432.8909
AIC (smaller is better)                 877.7818
AICC (smaller is better)                878.1275
BIC (smaller is better)                 898.9106

Algorithm converged.
                       Analysis Of Maximum Likelihood Parameter Estimates

Standard     Wald 95% Confidence          Wald
Parameter          DF    Estimate       Error           Limits           Chi-Square    Pr > ChiSq

Intercept           1      1.3710      0.2561      0.8691      1.8730         28.66        <.0001
child               1     -1.5153      0.1956     -1.8986     -1.1319         60.02        <.0001
camper        1     1      0.8791      0.2693      0.3513      1.4068         10.66        0.0011
camper        0     0      0.0000      0.0000      0.0000      0.0000           .           .
Dispersion          1      2.6788      0.4713      1.8974      3.7818

NOTE: The negative binomial dispersion parameter was estimated by maximum
likelihood.

Analysis Of Maximum Likelihood Zero Inflation Parameter Estimates

Standard       Wald 95%             Wald
Parameter  DF  Estimate     Error   Confidence Limits  Chi-Square  Pr > ChiSq

Intercept   1    1.6031    0.8365   -0.0364    3.2426        3.67      0.0553
persons     1   -1.6666    0.6793   -2.9979   -0.3352        6.02      0.0142

The output has a few components which are explained below.

• Model Information: General information about the data set, outcome variable, distribution and the number of observations used in the model.
• Class Level Information: For each categorical variable, the number of levels and how the levels are coded. The last displayed level will be the reference group in the model. In this example, it will be 0.
• Criteria For Assessing Goodness Of Fit: These measures are usually used for comparing models.
• Analysis Of Maximum Likelihood Parameter Estimates: Negative binomial part of the model, estimated using maximum likelihood.
• Analysis Of Maximum Likelihood Zero Inflation Parameter Estimates: Logistic regression part of the model, for estimating the probability of being an excessive zero.

Looking through the results of regression parameters we see the following:

• The predictors child and camper in the part of the negative binomial regression model predicting number of fish caught (count) are both significant predictors.
• The predictor person in the part of the logit model predicting excessive zeros is statistically significant.
• For these data, the expected change in log(count) for a one-unit increase in child is -1.515255.  This amounts to a 78% (1 - e-1.515255 = .78) decrease in the expected count for each additional child in the party holding other variables constant.
• Groups with campers (camper = 1) had an expected log(count) 0.879051 higher than groups without campers (camper = 0), i.e., the expected count of fish for a camper is about 2.41 (e0.879051 = 2.41) times higher than for a non-camper.
• The log odds of being an excessive zero would decrease by 1.67 for every additional person in the group. In other words, the more people in the group ,the less likely that the zero would be due to not gone fishing. Put it plainly, the larger the group the person was in, the more likely that the person went fishing.
• The estimate of the dispersion parameter is displayed with its confidence interval.  There seems enough indication of over dispersion, meaning that negative binomial model might be more appropriate.

We might want to compare the current zero-inflated negative binomial model with the plain negative binomial model, which can be done via, for example, Vuong test. Currently Vuong test is not a standard part of proc genmod, but a macro program is available from SAS that does the Vuong test. You can download this macro program following the link and store it on your hard drive. In this example, we saved the macro program in d:/work/dae directory and rename it as vuong.sas. To use the macro program, we use the %include statement. This macro program takes quite a few arguments shown below. We rerun the models to get produce these required input arguments. We have also used the statement store to store the estimates so we can do post-estimation using the same model via proc plm without having to rerun the model. With the zero-inflated negative binomial model, there are total of six regression parameters which includes the intercept, the regression coefficients for child and camper and the dispersion parameter for the negative binomial portion of the model as well as the intercept and regression coefficient for persons. The plain negative binomial regression model has a total of four regression parameters. The scale parameters (scale1 and scale2) are the dispersion parameters from each corresponding model.

%include "d:/work/dae/vuong.sas";
proc genmod data = fish order=data;
class camper;
model count = child camper /dist=zinb;
zeromodel persons;
output out=outzinb pred=predzinb pzero=p0;
store m1;
run;
proc genmod data = outzinb order=data;
class camper;
model count = child camper /dist=nb;
output out=out pred=prednb;
run;
%vuong(data=out, response=count,
model1=zinb, p1=predzinb, dist1=zinb, scale1=2.6788, pzero1=p0,
model2=nb,   p2=prednb,   dist2=nb,   scale2=3.9171,
nparm1=6,    nparm2=4)
The Vuong Macro                                                                                                                                                                   44

Model Information

Data Set                         out
Response                         count
Number of Observations Used      250
Model 1                          zinb
Distribution                  ZINB
Predicted Variable            predzinb
Number of Parameters          5
Scale                         2.6788
Zero-inflation Probability    p0
Log Likelihood                -432.89091
Model 2                          nb
Distribution                  NB
Predicted Variable            prednb
Number of Parameters          3
Scale                         3.9171
Log Likelihood                -439.7103

Vuong Test
H0: models are equally close to the true model
Ha: one of the models is closer to the true model

Preferred
Vuong Statistic            Z    Pr>|Z|      Model

Clarke Sign Test
H0: models are equally close to the true model
Ha: one of the models is closer to the true model

Preferred
Clarke Statistic           M    Pr>=|M|      Model



The output above shows the Vuong test followed by the Clarke Sign test. The positive values of the Z statistics for Vuong test indicate that it is the first model, the zero-inflated negative binomial model, which is closer to the true model. Both of these tests have the same null hypothesis and it happens that the two tests are not consistent with each other leading a weak support for the zero-inflated negative binomial model.

Now, let's try to understand the model better by using some of the post estimation commands. First off, we examine the distribution of the predicted probability of being an excessive zero by the number of persons in the group. We can see that the larger the group, the smaller the probability, meaning the more likely that the person went fishing.

proc means data = outzinb nway mean;
class persons;
var p0;
run;
                  N
persons    Obs            Mean
-----------------------------------
1     57       0.4841404

2     70       0.1505843

3     57       0.0324022

4     66       0.0062858
-----------------------------------

Since we have saved our model previous as m1 previously, we use proc plm  to get the predicted number of fish caught, comparing campers with non-campers given different number of children.  To get the predict counts we have used the option ilink (for inverse link).

proc plm source = m1;
run;
                                    camper Least Squares Means

Standard
Standard                                       Error of
camper     child    persons    Estimate       Error    z Value    Pr > |z|        Mean        Mean

1           0.00       2.53      2.2501      0.2037      11.05      <.0001      9.4887      1.9326
0           0.00       2.53      1.3710      0.2561       5.35      <.0001      3.9395      1.0090

Standard
Standard                                       Error of
camper     child    persons    Estimate       Error    z Value    Pr > |z|        Mean        Mean

1           1.00       2.53      0.7348      0.1932       3.80      0.0001      2.0852      0.4028
0           1.00       2.53     -0.1442      0.2342      -0.62      0.5381      0.8657      0.2028

Standard
Standard                                       Error of
camper     child    persons    Estimate       Error    z Value    Pr > |z|        Mean        Mean

1           2.00       2.53     -0.7804      0.3311      -2.36      0.0184      0.4582      0.1517
0           2.00       2.53     -1.6595      0.3474      -4.78      <.0001      0.1902     0.06608

Standard
Standard                                       Error of
camper     child    persons    Estimate       Error    z Value    Pr > |z|        Mean        Mean

1           3.00       2.53     -2.2957      0.5084      -4.52      <.0001      0.1007     0.05120
0           3.00       2.53     -3.1747      0.5128      -6.19      <.0001     0.04181     0.02144


Notice by default, SAS fixes the value of the predictor variable persons at its mean value. Next, we can also ask proc plm to plot the fitted values by camper variable.

ods graphics  / width=4in height=3in border=off;
proc plm source = m1;
effectplot slicefit (sliceby= camper);
run;
ods graphics off;

#### Things to consider

Here are some issues that you may want to consider in the course of your research analysis.

• Question about the over-dispersion parameter is in general a tricky one. A large over-dispersion parameter could be due to a miss-specified model or could be due to a real process with over-dispersion. Adding an over-dispersion problem does not necessarily improve a miss-specified model.
• The zinb model has two parts, a negative binomial count model and the logit model for predicting excess zeros, so you might want to review these Data Analysis Example pages, Negative Binomial Regression and Logit Regression.
• Since zinb has both a count model and a logit model, each of the two models should have good predictors. The two models do not necessarily need to use the same predictors.
• Problems of perfect prediction, separation or partial separation can occur in the logistic part of the zero-inflated model.
• Count data often use exposure variable to indicate the number of times the event could have happened. You can incorporate exposure into your model by using the exposure() option.
• It is not recommended that zero-inflated negative binomial models be applied to small samples. What constitutes a small sample does not seem to be clearly defined in the literature.
• Pseudo-R-squared values differ from OLS R-squareds, please see FAQ: What are pseudo R-squareds? for a discussion on this issue.

#### References

• Cameron, A. Colin and Trivedi, P.K. (2009) Microeconometrics using stata. College Station, TX: Stata Press.
• Long, J. Scott, & Freese, Jeremy (2006). Regression Models for Categorical Dependent Variables Using Stata (Second Edition). College Station, TX: Stata Press.
• Long, J. Scott (1997). Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, CA: Sage Publications.

modified on October 28, 2011

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